Prashant Srivastav - Rapida - blog.rapida.ai

Prashant Srivastav

Enabling enterprise to use LLM at Scale, Building RapidaAI

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Chat Completions vs Responses vs WebSocket: TTFT and TRT in Rapida

Chat Completions vs Responses vs WebSocket: TTFT and TRT in Rapida

In voice AI, 300–500ms latency differences completely change conversation quality. Yet most teams still benchmark only tokens/sec instead of end-to-end conversational responsiveness. LLMs are evolving fast, and so is OpenAI. Every few months, the model layer changes in a meaningful way: better reasoning, better tool calling, better streaming

Rapida Voice System Performance Benchmarks

This post documents latency and scaling behavior of Rapida under sustained concurrent voice traffic. The intent is to show how the system behaves under realistic production conditions rather than demo workloads. Rapida is an open source voice orchestration system designed to manage real time audio streaming, speech recognition, language model

Transforming Customer Service with Voice AI: From IVR to Intelligent Agents

Transforming Customer Service with Voice AI: From IVR to Intelligent Agents

Why legacy IVRs fall short Last week I called my bank and spent 4 minutes pressing buttons before a robot told me to call back during business hours. We can do better. Classic IVRs were designed to deflect calls with keypad trees. Today, customers expect fast, human-like help and seamless

Voice Agent Telemetry: Where Every Millisecond Counts

Voice Agent Telemetry: Where Every Millisecond Counts

When building real time voice systems, there's a moment we've all felt. That pause after you finish speaking, waiting for the AI to respond. It's a fraction of a second, but it breaks the flow. That delay is what separates fast enough from real

Scaling Voice AI in India: The Hard Part

Scaling Voice AI in India: The Hard Part

IRCTC proves voice AI can handle national-scale demand—but only if you design for the hard stuff first. Here’s the reality. Why India-Scale Voice Is Uniquely Hard 1) Language & Accents * Dozens of languages and dialects; heavy Hindi–English/Bengali–English switching. * Wide variance in accents, speech rates, and

Comparing Approaches for End-of-Speech Detection in Voice AI

Comparing Approaches for End-of-Speech Detection in Voice AI

While building RapidaAI, one of the biggest challenges we are navigating is figuring out when a user has actually finished speaking — especially across different languages, accents, and natural speaking styles. Get it wrong, and the conversation feels off: interrupt too soon, and the user has to repeat themselves; wait too

Voice is the growth engine in a $100B CPaaS future

Voice is the growth engine in a $100B CPaaS future

The #CPaaS market isn’t just growing, it is exploding. The CPaaS Acceleration Alliance notes that the market is worth $32 billion in 2025 and could reach $100 billion by 2030. And the analysts agree that growth hinges on how well providers integrate #AI and new channels to create intelligent

Revolutionise Your Customer Service with AI: Discover the Power of RAG as a service

Revolutionise Your Customer Service with AI: Discover the Power of RAG as a service

In today's customer service landscape, where accuracy is critical, a single misstep can lead to widespread dissatisfaction. The introduction of generative AI has marked a significant development in this sector, but it also introduces the challenge of AI "hallucinations" — misleading or incorrect AI-generated responses. Such errors

Introducing Our New AI-Powered Loan Underwriting Agent: A Game-Changer in Lending

Introducing Our New AI-Powered Loan Underwriting Agent: A Game-Changer in Lending

Join waitlist to get full access https://www.rapida.ai/ In the fast-evolving financial landscape, technology continuously reshapes how institutions operate and interact with their customers. Today, we’re thrilled to unveil the latest breakthrough in our lending services: an AI-powered Loan Underwriting Agent. This innovative tool can transform the

Fine-Tuning vs. Knowledge Base: Optimizing Large Language Models for Specific Use Cases

Fine-Tuning vs. Knowledge Base: Optimizing Large Language Models for Specific Use Cases

Introduction In the rapidly evolving field of artificial intelligence, specifically with large language models (LLMs) like GPT, businesses and developers are continually seeking ways to tailor these models to meet specific needs. Two primary methods stand out: fine-tuning and leveraging a knowledge base. Each approach has its distinct advantages and